The following article was written by Lorenzo Baietta a student at Brunel University London and presented at the International CAE Conference Poster Competition in Vicenza, Italy. Lorenzo’s work placed 6th overall and 1st among articles written by a single author. We’re thrilled for Lorenzo and excited to continue supporting universities and young engineers all over the world.
The continue research for engine efficiency improvements is one of the major challenges of the last decades, leading to the design of highly downsized boosted engines. Among other boosting strategies, turbocharging allows to recover part of the exhaust gas energy, improving the overall efficiency of the power unit. However, turbochargers lead to less responsive power units because of the widely known turbo-lag effect due to the inertia of the rotating parts in the system. With engine manufacturers testing different concepts to reduce this effect, for both commercial and motorsport applications, the work is about the development of a low inertia turbocharger axial turbine, evaluating pro and cons of several design solution. The idea is to initially evaluate the performance (mainly efficiency) difference between prismatic and twisted blades turbine for different size ranges. In fact, as one of the issue of axial turbines compared to radial ones is the production cost, the use of low aspect ratios blades, in such a way to minimize the difference between the use of 3D optimized turbines and prismatic turbines, should allow for more cost-effective solutions to be implemented.
After selecting a specific engine to develop the axial turbine, several CAE techniques were used to verify the idea and to obtain the best possible solution. The OEM turbocharger was 3D scanned, with a blue light technology stereoscopic optical system, to acquire accurate geometry data and calculate several properties. A 1D engine model, calibrated on the dyno, was used to calculate the aerothermal boundary conditions for the design of the turbine every 1000rpm from 1000 to 6000 to have all the required boundary conditions data to design/test the turbine at different engine operating points.
Several turbines were preliminary designed and optimized with AxSTREAM® and their performances were evaluated considering many parameters, mainly focusing on the reduction of the turbocharger spool-up time. The AxSTREAM® preliminary design module resulted crucial to compare the performance of over 1 million turbines allowing the comparison of the results with different loss models and a wide number on flow boundary conditions and geometrical constraints.
The generated turbine preliminary CAD and the scanned OEM turbine mesh were used along with CAM programs at an external company to estimate the production cost of different solutions. A final turbine design was chosen, among the pre-designed ones, to be validated with generation of complete maps within the AxSTREAM® streamline solver which allowed an initial verification of the suitability of the turbine for the desired application. A further optimization of the results was obtained with increasing precision CFD simulations in the AxSTREAM® Profiling and CFD modules. 2D cascade simulations were used to optimize the stator and rotor airfoils in the Profiling module. Then, in AxCFD™, axisymmetric CFD simulations were run at several operating points to quickly investigate the suitability of the generated design for the whole power unit operating range. To conclude, full 3D CFD and FEA simulations were conducted to obtain more accurate values and complete the design process of the turbine and finally compare the data of the newly designed turbine and the OEM one.
The results of the simulations demonstrated the superiority of the proposed turbine both from the spool-up factor (up to 2,17 times more than the OEM solution) and efficiency point of view, confirming the success of the project. A 17% cost rise for the turbine, which is even less when calculated in respect of the whole turbocharger cost, represents a small price for an average 167% spool-up factor gain.